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1. Use a layer that calls `self.add_loss(value)`. Docs: https://keras.io/api/losses/#the-addloss-api … And if you need access to labels, use the "endpoint layer" pattern: https://keras.io/examples/keras_recipes/endpoint_layer_pattern/ …
2. Override the `train_step` method of your Model class to implement your custom loss. Guide: https://keras.io/guides/customizing_what_happens_in_fit/ … A few examples: Metric learning: https://keras.io/examples/vision/metric_learning/ … Vanilla GAN: https://keras.io/examples/generative/dcgan_overriding_train_step/ … WGAN-GP: https://keras.io/examples/generative/wgan_gp/ … CycleGAN: https://keras.io/examples/generative/cyclegan/ …
3. Write a custom training loop from scratch. Guide: https://keras.io/guides/writing_a_training_loop_from_scratch/ … Remember that Keras defaults are only meant to cover the most common workflows -- for anything else, you have a range of increasingly advanced options. We call it "progressive disclosure of complexity"
How do I modify train_step if I need to handle a validation dataset? Do I need to write the full training loop from scratch?
You override test_step too
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